Due to advances in computing technology, businesses today are able to operate more efficiently when compared to substantially similar businesses only a few years ago. For example, internal networking enables employees of a company to communicate instantaneously by email, quickly transfer data files to disparate employees, manipulate data files, wirelessly communicate with databases, immediately exchange wireless text messages, share data relevant to a project to reduce duplications in work product, etc. Furthermore, advancements in technology have enabled factory applications to become partially or completely automated. For instance, operations that once required workers to put themselves proximate to heavy machinery and other various hazardous conditions can now be completed at a safe distance therefrom.
Further, imperfections associated with human action have been minimized through employment of highly precise machines. Many of these factory devices supply data related to manufacturing to databases that are accessible by system/process/project managers on a factory floor. For instance, sensors and associated software can detect a number of instances that a particular machine has completed an operation successfully and in a defined amount of time. Further, data from sensors can be delivered to a processing unit relating to system alarms. Thus, a factory automation system can review collected data and automatically and/or semi-automatically schedule maintenance of a device, replacement of a device, and other various procedures that relate to automating a food process and/or food quality.
The greater degree of automation and the drive toward increased throughput and productivity can now produce goods at a rate of hundreds to thousands of items each minute. This extremely high throughput and “hands-off” production places a much greater demand for continuous, in situ product and process quality sensors in various areas such as, but not limited to the food industry, biological agent detection, incubation, anti-terrorism, anti-contamination, bio-sensing, pharmaceuticals, bio-fluids, animal food, packaging, beverages, microbial detection, by-product, waste material, batch processes, up-stream food chemical/additive manufacturing, grower/picker (e.g., manual and automated), food distributor, food manufacturer and packager, environmental monitoring, air quality monitoring, in vivo monitoring and diagnostics, any suitable environment that can utilize sensor technology, etc.
For example, typical food processing plants employ periodic sample extraction and laboratory analysis to monitor product quality. In-process sensors to monitor quality are lightly used. In spite of sample testing, food recalls due to in-process contamination or process upsets continue. It is clear that the nation's food supply is also vulnerable to malicious acts such as from food process employees, suppliers, or foreign nationals such as terrorists. Protection of the food supply from accidental or malicious alteration will be enhanced with expanded continuous in situ monitoring and analysis of food process, food products, and associated machinery. Similarly, the nation's water supply is considered a critical element in our national infrastructure. Municipal and local water supplies may be affected by bacteria and other contaminants and may also be vulnerable to malicious acts. The unique character of our water supply may make detecting and isolating a source of hazardous materials difficult to rapidly detect and isolate.
Conventional systems and/or methodologies utilized to obtain measurements of parameters which need a substantial amount of time for sufficient measurement, require the medium to be extracted from a machine and/or process, packaged and sent to a laboratory, and thereafter tested in a laboratory environment. Such testing results in significant delay in measurement, and can therefore result in delay application and/or process modification if such actions are required. These delays can contribute to accelerated failure of a process, machine, application, and/or degradation of food quality.
While sample extraction and batch measurements can potentially provide superior accuracy, there are associated problems with this technique. These can include: cost (staff, supplies), test equipment acquisition, maintenance, repair, and calibration, training for operator skills and specification equipment, contamination of samples, test availability, worker safety to extract sample, sample disposal, product scrap produced during sample extraction and testing, and the inability to correlate measurement results with dynamic process control or with multiple sensors distributed in the process related to the specific environment the sensor(s) are deployed. Particular processes and/or machinery requiring maintenance based on sensors can be located at positions within a factory and/or plant that are difficult to reach and therefore require a significant amount of the maintenance engineer's time to perform such maintenance. Furthermore, the maintenance engineer is prone to human error (e.g., add incorrect fluids and/or fluid additives to a particular machine or machine component, sample vials may be contaminated prior to sampling, as well as provide the machine or machine component with an over-abundance of fluid, lube oil). These and other similar maintenance errors can result in accelerated failure of the machine, process, and the degradation of product quality and the potential release of dangerous substances in the supply. Product recalls are not uncommon. Many of these recalls are due to contamination, inappropriate process input materials, or process upsets. Serious illness and death has also occurred associated with the above deficiencies.
Intelligent sensors comprised of several or more sensing elements and embedded processors are becoming more prevalent. There are emerging smart sensor standards that can promote the development and deployment of intelligent and/or wireless sensor systems. Conventionally, defining and configuring intelligent sensor systems is primarily done at design time with operating parameters specified during manufacturing and/or device configuration. Typical sensor systems are not practical to develop, manufacture, stock, and support a plurality of designs to accommodate the wide and varying application requirements and sensing needs. As a result, sensors are typically “dumb” sensors or provided as integrated systems coupled to a sensor processor module. Alternatively, smart sensor systems are costly and pre-packaged for specific types of applications.
However, it can be extremely costly and difficult to tailor a sensor system to different applications and/or environments. In particular, a food processing environment requires an environment-specific sensor or sensor system tailored to provide real-time data related to a particular parameter. On the contrary, a pharmaceutical environment requires a different sensor or sensor system that is specifically tailored to the environment to provide real-time data related to a parameter associated therewith. There are specific skills required to design and deploy sensor systems in general. These skills may include analog and digital electronics, software, communications, signal processing, data acquisition, software integration, database, packaging, and materials knowledge. Furthermore, deploying a sensor or sensor system in different environments or applications often includes additional application specific knowledge such as safety standards, compliance requirements, and process knowledge. In other words, there are various environments in which real-time data sensors can be employed, yet each environment mandates tailored sensor systems for different applications (e.g., communications mode, memory capacity, power supply, etc.).